--- OA_place: publisher _id: '21198' abstract: - lang: eng text: "In recent years there has been a massive increase in the amount of data generated in a\r\ndecentralized manner. Ever more powerful edge devices, such as smartphones, have become\r\nubiquitous in most societies on earth. Through text typed, photos taken and apps used,\r\nthese devices, which we refer to as clients, generate enormous amounts of high quality and\r\ncomplex data. Moreover, the nature of these devices means the data they generate is often\r\nsensitive and privacy concerns prevent it being gathered and stored in a central location. This\r\npresents a challenge to the modern machine learning paradigm that requires central access\r\nto large amounts of data. Federated learning (FL) has emerged as one of the answers to\r\nthis problem. Rather than bringing the data to the model, FL sends the model to the data.\r\nModel training takes place on device, with periodically synchronized updates, allowing data to\r\nremain locally stored. While this approach offers significant privacy advantages it comes with\r\nits own set of unique challenges. These include: data heterogeneity, the notion that different\r\ndevices generate data in distinct ways which can negatively impact training dynamics; systems\r\nheterogeneity, meaning that different devices may have differing hardware specifications; high\r\ncommunication costs, which are induced by the repeated transferring of models over the\r\nnetwork and low device computational power, which limits the use of larger models on device.\r\nIn this thesis we present a range of methods for federated learning. We focus primarily on\r\nthe challenge of data heterogeneity, though the methods presented are designed to be well\r\nadapted to the other challenges of a federated setting, such as the constraints of limited\r\ncompute and communication overhead. We first present a method for explicitly modeling client\r\ndata heterogeneity. The approach formulates clients as samples from a certain probability\r\ndistribution and infers the parameters of this distribution from the available training clients.\r\nThis learned distribution then represents the heterogeneity present among the clients and can\r\nbe sampled from in order to create new simulated clients that are similar to the real clients we\r\nhave observed so far. Following this we present two methods for directly dealing with data\r\nheterogeneity through personalization. Highly heterogeneous client data distributions can mean\r\nthat learning a single global model becomes suboptimal, and some form of personalization of\r\nmodels to each individual client is required. Our approaches are based around hypernetworks,\r\nwhich we use to generate personalized model parameters without the need for additional\r\ntraining or finetuning. In the first approach we focus on generating full parameterizations of\r\nclient models using learned embeddings of client data and labels, with a hypernetwork located\r\non the central server. In the second approach we address the more challenging scenario where\r\nwe want to generate a personalized model for a client without any label information. The\r\nhypernetwork is trained to generate a low dimensional representation of a client’s personalized\r\nmodel parameters, allowing it to be transferred to and run on the client devices. In our final\r\npresented method, we change our focus and rather than aim to directly address the challenge\r\nof data heterogeneity, we instead ensure we are unaffected by it. This is done in the context\r\nof k-means clustering and we present a method for federated clustering with a focus on added\r\nprivacy guarantees." acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was funded in part by the Austrian Science Fund (FWF)\r\n[10.55776/COE12]. Furthermore, the candidate acknowledges the support from the Scientific\r\nService Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp)." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott citation: ama: Scott JA. Data heterogeneity and personalization in federated learning. 2026. doi:10.15479/AT-ISTA-21198 apa: Scott, J. A. (2026). Data heterogeneity and personalization in federated learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21198 chicago: Scott, Jonathan A. “Data Heterogeneity and Personalization in Federated Learning.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21198. ieee: J. A. Scott, “Data heterogeneity and personalization in federated learning,” Institute of Science and Technology Austria, 2026. ista: Scott JA. 2026. Data heterogeneity and personalization in federated learning. Institute of Science and Technology Austria. mla: Scott, Jonathan A. Data Heterogeneity and Personalization in Federated Learning. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21198. short: J.A. Scott, Data Heterogeneity and Personalization in Federated Learning, Institute of Science and Technology Austria, 2026. corr_author: '1' date_created: 2026-02-09T14:59:53Z date_published: 2026-02-09T00:00:00Z date_updated: 2026-04-07T11:46:11Z day: '09' ddc: - '005' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/AT-ISTA-21198 file: - access_level: closed checksum: 121c1d968bd86f3630aa7e81d5bbbcb0 content_type: application/zip creator: jscott date_created: 2026-02-17T11:46:22Z date_updated: 2026-02-17T11:46:22Z file_id: '21298' file_name: 2026_Scott_Jonathan_Thesis_Source.zip file_size: 272379252 relation: source_file - access_level: open_access checksum: 6e3e08ba474bbee8511cc8a839ab2077 content_type: application/pdf creator: jscott date_created: 2026-02-27T10:25:41Z date_updated: 2026-02-27T10:25:41Z file_id: '21366' file_name: 2026_Jonathan_Scott_Thesis.pdf file_size: 15220298 relation: main_file success: 1 file_date_updated: 2026-02-27T10:25:41Z has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '158' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '20819' relation: part_of_dissertation status: public - id: '17411' relation: part_of_dissertation status: public - id: '18120' relation: part_of_dissertation status: public - id: '21207' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Data heterogeneity and personalization in federated learning type: dissertation user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd year: '2026' ... --- OA_place: publisher OA_type: gold _id: '20819' abstract: - lang: eng text: "Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are increasingly being produced and stored in a distributed way, e.g. on edge devices, and privacy concerns prevent it from being transferred to a central server. To address this challenge, we present FedDP-KMeans, a new algorithm for \r\n-means clustering that is fully-federated as well as differentially private. Our approach leverages (potentially small and out-of-distribution) server-side data to overcome the primary challenge of differentially private clustering methods: the need for a good initialization. Combining our initialization with a simple federated DP-Lloyds algorithm we obtain an algorithm that achieves excellent results on synthetic and real-world benchmark tasks. We also provide a theoretical analysis of our method that provides bounds on the convergence speed and cluster identification success." acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was funded in part by the Austrian Science Fund (FWF) [10.55776/COE12] and supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp).\r\n" alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: David full_name: Saulpic, David id: f8e48cf0-b0ff-11ed-b0e9-b4c35598f964 last_name: Saulpic citation: ama: 'Scott JA, Lampert C, Saulpic D. Differentially private federated k-means clustering with server-side data. In: 42nd International Conference on Machine Learning. Vol 267. ML Research Press; 2025:53757-53790.' apa: 'Scott, J. A., Lampert, C., & Saulpic, D. (2025). Differentially private federated k-means clustering with server-side data. In 42nd International Conference on Machine Learning (Vol. 267, pp. 53757–53790). Vancouver, Canada: ML Research Press.' chicago: Scott, Jonathan A, Christoph Lampert, and David Saulpic. “Differentially Private Federated K-Means Clustering with Server-Side Data.” In 42nd International Conference on Machine Learning, 267:53757–90. ML Research Press, 2025. ieee: J. A. Scott, C. Lampert, and D. Saulpic, “Differentially private federated k-means clustering with server-side data,” in 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 53757–53790. ista: 'Scott JA, Lampert C, Saulpic D. 2025. Differentially private federated k-means clustering with server-side data. 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 53757–53790.' mla: Scott, Jonathan A., et al. “Differentially Private Federated K-Means Clustering with Server-Side Data.” 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 53757–90. short: J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790. conference: end_date: 2025-07-19 location: Vancouver, Canada name: 'ICML: International Conference on Machine Learning' start_date: 2025-07-13 corr_author: '1' date_created: 2025-12-14T23:02:05Z date_published: 2025-05-01T00:00:00Z date_updated: 2026-04-07T11:46:11Z day: '01' ddc: - '000' department: - _id: ChLa - _id: MoHe external_id: arxiv: - '2506.05408' file: - access_level: open_access checksum: 815b32b463023ca21e569c2158745c15 content_type: application/pdf creator: dernst date_created: 2025-12-16T12:38:29Z date_updated: 2025-12-16T12:38:29Z file_id: '20829' file_name: 2025_ICML_Scott.pdf file_size: 746612 relation: main_file success: 1 file_date_updated: 2025-12-16T12:38:29Z has_accepted_license: '1' intvolume: ' 267' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 53757-53790 publication: 42nd International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '21198' relation: dissertation_contains status: public scopus_import: '1' status: public title: Differentially private federated k-means clustering with server-side data tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 267 year: '2025' ... --- OA_place: repository _id: '21207' abstract: - lang: eng text: Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approaches require a client to have labeled data for training or finetuning in order to obtain their own personalized model. In this paper we address this by proposing FLowDUP, a novel method that is able to generate a personalized model using only a forward pass with unlabeled data. The generated model parameters reside in a low-dimensional subspace, enabling efficient communication and computation. FLowDUP's learning objective is theoretically motivated by our new transductive multi-task PAC-Bayesian generalization bound, that provides performance guarantees for unlabeled clients. The objective is structured in such a way that it allows both clients with labeled data and clients with only unlabeled data to contribute to the training process. To supplement our theoretical results we carry out a thorough experimental evaluation of FLowDUP, demonstrating strong empirical performance on a range of datasets with differing sorts of statistically heterogeneous clients. Through numerous ablation studies, we test the efficacy of the individual components of the method. article_processing_charge: No author: - first_name: Hossein full_name: Zakerinia, Hossein id: 653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4 last_name: Zakerinia orcid: 0009-0007-3977-6462 - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Zakerinia H, Scott JA, Lampert C. Federated learning with unlabeled clients: Personalization can happen in low dimensions. arXiv. doi:10.48550/ARXIV.2505.15579' apa: 'Zakerinia, H., Scott, J. A., & Lampert, C. (n.d.). Federated learning with unlabeled clients: Personalization can happen in low dimensions. arXiv. https://doi.org/10.48550/ARXIV.2505.15579' chicago: 'Zakerinia, Hossein, Jonathan A Scott, and Christoph Lampert. “Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2505.15579.' ieee: 'H. Zakerinia, J. A. Scott, and C. Lampert, “Federated learning with unlabeled clients: Personalization can happen in low dimensions,” arXiv. .' ista: 'Zakerinia H, Scott JA, Lampert C. Federated learning with unlabeled clients: Personalization can happen in low dimensions. arXiv, 10.48550/ARXIV.2505.15579.' mla: 'Zakerinia, Hossein, et al. “Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions.” ArXiv, doi:10.48550/ARXIV.2505.15579.' short: H. Zakerinia, J.A. Scott, C. Lampert, ArXiv (n.d.). corr_author: '1' date_created: 2026-02-10T08:20:59Z date_published: 2025-05-21T00:00:00Z date_updated: 2026-04-07T11:46:11Z day: '21' department: - _id: ChLa doi: 10.48550/ARXIV.2505.15579 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2505.15579 month: '05' oa: 1 oa_version: Preprint publication: arXiv publication_status: draft related_material: record: - id: '21198' relation: dissertation_contains status: public status: public title: 'Federated learning with unlabeled clients: Personalization can happen in low dimensions' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2025' ... --- _id: '17411' abstract: - lang: eng text: "We present PeFLL, a new personalized federated learning algorithm that improves\r\nover the state-of-the-art in three aspects: 1) it produces more accurate models,\r\nespecially in the low-data regime, and not only for clients present during its\r\ntraining phase, but also for any that may emerge in the future; 2) it reduces the\r\namount of on-client computation and client-server communication by providing\r\nfuture clients with ready-to-use personalized models that require no additional\r\nfinetuning or optimization; 3) it comes with theoretical guarantees that establish\r\ngeneralization from the observed clients to future ones.\r\nAt the core of PeFLL lies a learning-to-learn approach that jointly trains an\r\nembedding network and a hypernetwork. The embedding network is used to\r\nrepresent clients in a latent descriptor space in a way that reflects their similarity\r\nto each other. The hypernetwork takes as input such descriptors and outputs the\r\nparameters of fully personalized client models. In combination, both networks\r\nconstitute a learning algorithm that achieves state-of-the-art performance in several\r\npersonalized federated learning benchmarks" acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp).\r\n" article_processing_charge: No arxiv: 1 author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Hossein full_name: Zakerinia, Hossein id: 653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4 last_name: Zakerinia orcid: 0009-0007-3977-6462 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Scott JA, Zakerinia H, Lampert C. PEFLL: Personalized federated learning by learning to learn. In: 12th International Conference on Learning Representations. OpenReview; 2024.' apa: 'Scott, J. A., Zakerinia, H., & Lampert, C. (2024). PEFLL: Personalized federated learning by learning to learn. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.' chicago: 'Scott, Jonathan A, Hossein Zakerinia, and Christoph Lampert. “PEFLL: Personalized Federated Learning by Learning to Learn.” In 12th International Conference on Learning Representations. OpenReview, 2024.' ieee: 'J. A. Scott, H. Zakerinia, and C. Lampert, “PEFLL: Personalized federated learning by learning to learn,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.' ista: 'Scott JA, Zakerinia H, Lampert C. 2024. PEFLL: Personalized federated learning by learning to learn. 12th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Scott, Jonathan A., et al. “PEFLL: Personalized Federated Learning by Learning to Learn.” 12th International Conference on Learning Representations, OpenReview, 2024.' short: J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024. conference: end_date: 2024-03-07 location: Vienna, Austria name: 'ICLR: International Conference on Learning Representations' start_date: 2024-03-07 corr_author: '1' date_created: 2024-08-11T22:01:12Z date_published: 2024-03-07T00:00:00Z date_updated: 2026-04-07T11:46:11Z day: '07' ddc: - '000' department: - _id: ChLa external_id: arxiv: - '2306.05515' file: - access_level: open_access checksum: 81b7ea2e667adaf9c7a7b6b376b1f251 content_type: application/pdf creator: dernst date_created: 2024-08-12T07:38:06Z date_updated: 2024-08-12T07:38:06Z file_id: '17415' file_name: 2024_ICLR_Scott.pdf file_size: 1029219 relation: main_file success: 1 file_date_updated: 2024-08-12T07:38:06Z has_accepted_license: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: 12th International Conference on Learning Representations publication_status: published publisher: OpenReview quality_controlled: '1' related_material: record: - id: '21198' relation: dissertation_contains status: public scopus_import: '1' status: public title: 'PEFLL: Personalized federated learning by learning to learn' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- _id: '18120' abstract: - lang: eng text: In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning that can be done, making it challenging to obtain good performance on a given task. Server-side proxy data can be used to run training simulations, for instance for hyperparameter tuning. This can greatly speed up the training pipeline by reducing the number of tuning runs to be performed overall on the true clients. However, it is challenging to ensure that these simulations accurately reflect the dynamics of the real federated training. In particular, the proxy data used for simulations often comes as a single centralized dataset without a partition into distinct clients, and partitioning this data in a naive way can lead to simulations that poorly reflect real federated training. In this paper we address the challenge of how to partition centralized data in a way that reflects the statistical heterogeneity of the true federated clients. We propose a fully federated, theoretically justified, algorithm that efficiently learns the distribution of the true clients and observe improved server-side simulations when using the inferred distribution to create simulated clients from the centralized data. acknowledgement: 'We would like to thank: Mona Chitnis and everyone in the Private Federated Learning team at Apple for their help and support throughout the entire project; Audra McMillan, Martin Pelikan, Anosh Raj and Barry Theobold for feedback on the initial versions of the paper; and Christoph Lampert for valuable feedback on the paper structure and suggestions for additional experiments.' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Áine full_name: Cahill, Áine last_name: Cahill citation: ama: 'Scott JA, Cahill Á. Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:44012-44037.' apa: 'Scott, J. A., & Cahill, Á. (2024). Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 44012–44037). Vienna, Austria: ML Research Press.' chicago: Scott, Jonathan A, and Áine Cahill. “Improved Modelling of Federated Datasets Using Mixtures-of-Dirichlet-Multinomials.” In Proceedings of the 41st International Conference on Machine Learning, 235:44012–37. ML Research Press, 2024. ieee: J. A. Scott and Á. Cahill, “Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 44012–44037. ista: 'Scott JA, Cahill Á. 2024. Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 44012–44037.' mla: Scott, Jonathan A., and Áine Cahill. “Improved Modelling of Federated Datasets Using Mixtures-of-Dirichlet-Multinomials.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 44012–37. short: J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 44012–44037. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' date_created: 2024-09-22T22:01:45Z date_published: 2024-09-01T00:00:00Z date_updated: 2026-04-07T11:46:11Z day: '01' department: - _id: ChLa external_id: arxiv: - '2406.02416' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.02416 month: '09' oa: 1 oa_version: Preprint page: 44012-44037 publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '21198' relation: dissertation_contains status: public scopus_import: '1' status: public title: Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '12660' abstract: - lang: eng text: 'We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches.' alternative_title: - TMLR article_processing_charge: No arxiv: 1 author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Michelle X full_name: Yeo, Michelle X id: 2D82B818-F248-11E8-B48F-1D18A9856A87 last_name: Yeo orcid: 0009-0001-3676-4809 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Scott JA, Yeo MX, Lampert C. Cross-client label propagation for transductive and semi-supervised federated learning. In: Transactions in Machine Learning. Curran Associates; 2023.' apa: Scott, J. A., Yeo, M. X., & Lampert, C. (2023). Cross-client label propagation for transductive and semi-supervised federated learning. In Transactions in Machine Learning. Curran Associates. chicago: Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive and Semi-Supervised Federated Learning.” In Transactions in Machine Learning. Curran Associates, 2023. ieee: J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client label propagation for transductive and semi-supervised federated learning,” in Transactions in Machine Learning, 2023. ista: Scott JA, Yeo MX, Lampert C. 2023. Cross-client label propagation for transductive and semi-supervised federated learning. Transactions in Machine Learning. , TMLR, . mla: Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive and Semi-Supervised Federated Learning.” Transactions in Machine Learning, Curran Associates, 2023. short: J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning, Curran Associates, 2023. corr_author: '1' date_created: 2023-02-20T08:21:50Z date_published: 2023-11-27T00:00:00Z date_updated: 2025-02-04T08:32:19Z day: '27' ddc: - '004' department: - _id: ChLa external_id: arxiv: - '2210.06434' file: - access_level: open_access checksum: aa322ad91cbd229f5cafe6733a119bd1 content_type: application/pdf creator: dernst date_created: 2025-02-04T08:30:05Z date_updated: 2025-02-04T08:30:05Z file_id: '18990' file_name: 2023_TMLR_Scott.pdf file_size: 553717 relation: main_file success: 1 file_date_updated: 2025-02-04T08:30:05Z has_accepted_license: '1' language: - iso: eng month: '11' oa: 1 oa_version: Preprint publication: Transactions in Machine Learning publication_identifier: issn: - 2835-8856 publication_status: published publisher: Curran Associates quality_controlled: '1' related_material: link: - relation: software url: https://github.com/jonnyascott/xclp status: public title: Cross-client label propagation for transductive and semi-supervised federated learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ...